# Crusoe Cloud: infrastructure or managed inference?

Distinguish Crusoe Infrastructure Cloud and Managed AI: self-managed VMs and clusters, managed deployments, payment units and model responsibilities.

Author: Retrivium team
Updated: 2026-11-01
Canonical URL: https://retrivium.ai/journal/crusoe-cloud-guide/
Language: en

Crusoe Cloud offers several AI-workload formats. Start by choosing the product: infrastructure the team manages or a service handling part of model deployment. Without that decision, cost comparisons can become mismatched.

Current to 1 November 2026, this review uses official pages. It contains no independent capacity test, fixed tariff or estimate of future token sales.

## Separate Infrastructure Cloud and Managed AI

[Crusoe documentation](https://docs.crusoe.ai/) distinguishes infrastructure and managed offerings. Consider VMs and clusters for self-managed environments, and the relevant AI services for another management level. Confirm the boundary within the actual offer.

A dedicated endpoint is not a physical server with unrestricted settings. Conversely, receiving compute does not mean someone operates the application. Record what the team does and what the provider product includes.

## Connect billing with the resource unit

[Crusoe pricing](https://www.crusoe.ai/cloud/pricing) separates compute and AI products. Distinguish charging for a device, configuration, dedicated deployment and model use. GPU-hour and token-volume numbers cannot be compared without the request profile.

For a duration commitment, include partial-utilisation scenarios. For flexible launches, check related costs and how billing ends. Another product's proposal cannot replace the project's commercial estimate.

The reviewed compute catalogue includes NVIDIA configurations such as H100, H200 and B200, alongside AMD directions. Moving between them requires engine-support verification. Hardware options aid selection without making artifacts interchangeable.

## Define the team's requirements

Infrastructure needs deployment, updating, monitoring and recovery skills. Managed arrangements still require model selection, quality evaluation and integration. Inspect observability and parameter controls before beginning.

For distributed work, clarify network and device arrangements. An individual interactive endpoint may prioritise latency, scaling and idle behaviour. Derive requirements from the application rather than interest in the largest accelerators.

## Compare two approaches

A hypothetical team already operates a model but wants less maintenance. It tests its engine on infrastructure and separately evaluates a suitable managed deployment with the same requests and output requirements.

Report quality, latency, restrictions and specialist work. Extra manual operation belongs in the budget. Time savings cannot be assumed before testing model updates, diagnostics and exit with preserved artifacts.

## Apply findings to your pool

This analysis clarifies operating responsibility. Owned hardware needs separate site assessment and agreement.

Read [operating responsibilities](/journal/gpu-pool-onboarding/) and [specialist clouds versus hyperscalers](/journal/specialist-vs-hyperscaler-gpu/).